Run-time reference clustering for cache performance optimization

Abstract

We introduce a method for improving the cache performance of irregular computations in which data are referenced through run-time defined indirection arrays. Such computations often arise in scientific problems. The presented method, called Run-Time Reference Clustering (RTRC), is a run-time analog of a compile-time blocking used for dense matrix problems. RTRC uses the data partitioning and re-mapping techniques that are a part of distributed memory multi-processor codes designed to minimize interprocessor communication. Re-mapping each set of local data decreases cache-misses the same way remapping the global data decreases off-processor references. We demonstrate the applicability and performance of the RTRC technique on several prevalent applications: Sparse Matrix-Vector Multiply, Particle-In-Cell, and CHARMMlike codes. Performance results on SPARC-20, SP-2, and T3-D processors show that single node execution performance can be improved by as much as 35%.